In this section, we first conduct a full study of our classification and localization distillation methods on several compact detectors, and then compare our classifier-to-detector approach to the state-of-the-art detector-to-detector ones.
Finally, we perform an extensive ablation study of our method and analyze how it improves the class recognition and localization in object detection. All models are trained and evaluated on MS COCO2017 [1]}, which contains over 118k images for training and 5k images for validation (minival) depicting 80 foreground object classes. Our implementation is based on mmdetection [2]} with Pytorch [3]}. Otherwise specified, we take the ResNet50 as the classification teacher.
We will use the same teacher for all two-stage Faster RCNNs and one-stage RetinaNets in our classifier-to-detector distillation method. We consider this to be an advantage of our method, since it lets us use the same teacher for multiple detectors. To train this classification teacher, we use the losses from Faster RCNN and RetinaNet frameworks jointly. Since SSDs use different data augmentation, we train another ResNet50 classification teacher for them.
Additional experimental details on how to train our classification teachers are provided in the supplementary material.
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